This proposal presents an agenda for collaborative research on the statistical analysis of longitudinal data with applications in demography, economics and medicine. A key goal of the proposed research is development of nonparametric, data sensitive procedures that enable analysts to incorporate and test th predictions of the relevant social or biological theories. We seek to improve on previous, highly parametric statistical procedures which incorporate considerable restrictions with little or no subject matter justification into estimation procedures. We document that this approach which is widely adopted obscures the data and produces empirical estimates that are sensitive to arbitrary parametric assumptions. Our proposal is not solely methodological in focus. We also plan to develop new theoretical and empirical dynamic models of labor supply. The confrontation of the new statistical and economic models with the data will enrich our understanding of labor force activity and will keep the methodological studies oriented toward empirical applications. Statistical models of thekind we expect to develop improve on methodologies that are routinely used in estimation of fertility decisions, contraception choice decisions, marriage and divorce decisions, and almost all statistical models for the analysis of longitudinal data. They are also used in economic and sociological models of decisions about school leaving, career development, savings-consumption decisions, in the analysis of contagious disease, and in studies of mortality and morbidity rates. The statistical procedures we will develop here can be applied to a wide range of studies that use data on continuous or discrete event histories over the lifetime.